Accelerating Hyperparameter Tuning of a Deep Learning Model for Remote Sensing Image Classification

Marcel Aach, Rocco Sedona, Andreas Lintermann, Gabriele Cavallaro, Helmut Neukirchen, Morris Riedel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Deep Learning models have proven necessary in dealing with the challenges posed by the continuous growth of data volume acquired from satellites and the increasing complexity of new Remote Sensing applications. To obtain the best performance from such models, it is necessary to fine-tune their hyperparameters. Since the models might have massive amounts of parameters that need to be tuned, this process requires many computational resources. In this work, a method to accelerate hyperparameter optimization on a High-Performance Computing system is proposed. The data batch size is increased during the training, leading to a more efficient execution on Graphics Processing Units. The experimental results confirm that this method reduces the runtime of the hyperparameter optimization step by a factor of 3 while achieving the same validation accuracy as a standard training procedure with a fixed batch size.

Original languageEnglish
Title of host publicationIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781665427920
Publication statusPublished - 17 Jul 2022
Event2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia
Duration: 17 Jul 202222 Jul 2022

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)


Conference2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
CityKuala Lumpur

Bibliographical note

Funding Information:
This work was performed in the Center of Excellence (CoE) Research on AI-and Simulation-Based Engineering at Exascale (RAISE) receiving funding from EU’s Horizon 2020 Research and Innovation Framework Programme H2020-INFRAEDI-2019-1 under grant agreement no. 951733. The authors gratefully acknowledge the computing time granted by the JARA Vergabegremium and provided on the JARA Partition part of the supercomputer JURECA at Forschungszentrum Jülich.

Publisher Copyright:
© 2022 IEEE.

Other keywords

  • Batch Size
  • Deep Learning
  • High-Performance Computing
  • Hyperparameter Tuning
  • Remote Sensing


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